Create app.py

app.py

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+import gradio as gr
+import torchvision.transforms as transforms
+from torchvision import models
+from PIL import Image
+
+# Load a pre-trained ResNet model
+model = models.resnet50(pretrained=True)
+model.eval()
+transform = transforms.Compose([transforms.Resize(256),
+                                transforms.CenterCrop(224),
+                                transforms.ToTensor(),
+                                transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                                                     std=[0.229, 0.224, 0.225])])
+
+# Define a function to classify an image
+def classify_image(input_image):
+    img = Image.open(input_image)
+    img = transform(img).unsqueeze(0)
+    with torch.no_grad():
+        outputs = model(img)
+        _, predicted_class = outputs.max(1)
+    return class_names[predicted_class.item()]
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=classify_image,
+    inputs=gr.inputs.Image(type="file", label="Upload an Image"),
+    outputs=gr.outputs.Textbox(label="Predicted Class"),
+    live=True,
+    theme="default",
+    title="Image Classification with ResNet",
+)
+
+# Launch the Gradio interface
+iface.launch()
+